"""simple docstring"""





def   lowerCamelCase_(__SCREAMING_SNAKE_CASE	)->     int:
							_SCREAMING_SNAKE_CASE		:    str      	=     len(__SCREAMING_SNAKE_CASE	)
							_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     len(matrix[0]	)
							_SCREAMING_SNAKE_CASE		:    Optional[Any]      	=     min(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE	)

							for row in range(__SCREAMING_SNAKE_CASE	):
														# Check if diagonal element is not zero
														if matrix[row][row] != 0:
																					# Eliminate all the elements below the diagonal
																					for col in range(row + 1  ,						__SCREAMING_SNAKE_CASE	):
																												_SCREAMING_SNAKE_CASE		:    Dict      	=     matrix[col][row] / matrix[row][row]
																												for i in range(__SCREAMING_SNAKE_CASE  ,						__SCREAMING_SNAKE_CASE	):
																																			matrix[col][i] -= multiplier * matrix[row][i]
														else:
																					# Find a non-zero diagonal element to swap rows
																					_SCREAMING_SNAKE_CASE		:    List[Any]      	=     True
																					for i in range(row + 1  ,						__SCREAMING_SNAKE_CASE	):
																												if matrix[i][row] != 0:
																																			_SCREAMING_SNAKE_CASE		:    Tuple      	=     matrix[i], matrix[row]
																																			_SCREAMING_SNAKE_CASE		:    List[Any]      	=     False
																																			break
																					if reduce:
																												rank -= 1
																												for i in range(__SCREAMING_SNAKE_CASE	):
																																			_SCREAMING_SNAKE_CASE		:    Optional[int]      	=     matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
																					row -= 1

							return rank


if __name__ == "__main__":
			import doctest

			doctest.testmod()







'''simple docstring'''



import json
import os
import subprocess
import unittest
from ast import literal_eval

import pytest
from parameterized import parameterized, parameterized_class

from . import is_sagemaker_available


if is_sagemaker_available():
							from sagemaker import Session, TrainingJobAnalytics
							from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
    literal_eval(os.getenv('TEST_SAGEMAKER'     ,					'False'      )      ) is not True     ,					reason='Skipping test because should only be run when releasing minor transformers version'     ,					)
@pytest.mark.usefixtures('sm_env'      )
@parameterized_class(
    [
        {
            'framework': 'pytorch',
            'script': 'run_glue_model_parallelism.py',
            'model_name_or_path': 'roberta-large',
            'instance_type': 'ml.p3dn.24xlarge',
            'results': {'train_runtime': 1_6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
        {
            'framework': 'pytorch',
            'script': 'run_glue.py',
            'model_name_or_path': 'roberta-large',
            'instance_type': 'ml.p3dn.24xlarge',
            'results': {'train_runtime': 1_6_0_0, 'eval_accuracy': 0.3, 'eval_loss': 1.2},
        },
    ]      )
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													if self.framework == "pytorch":
																				subprocess.run(
																				    F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split()					,     encoding='''utf-8'''					,     check=lowerCAmelCase__					,     )
													assert hasattr(self					,     '''env'''					)







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   List[Any]					):

													"""simple docstring"""
													# configuration for running training on smdistributed Model Parallel
													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''enabled''': True,
													    '''processes_per_host''': 8,
													}
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      {
													    '''enabled''': True,
													    '''parameters''': {
													        '''microbatches''': 4,
													        '''placement_strategy''': '''spread''',
													        '''pipeline''': '''interleaved''',
													        '''optimize''': '''speed''',
													        '''partitions''': 4,
													        '''ddp''': True,
													    },
													}

													SCREAMING_SNAKE_CASE       :   str         =      {'''smdistributed''': {'''modelparallel''': smp_options}, '''mpi''': mpi_options}

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      '''trainer''' if self.script == '''run_glue.py''' else '''smtrainer'''
													# creates estimator
													return HuggingFace(
													    entry_point=self.script					,     source_dir=self.env.test_path					,     role=self.env.role					,     image_uri=self.env.image_uri					,     base_job_name=F"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}"""					,     instance_count=lowerCAmelCase__					,     instance_type=self.instance_type					,     debugger_hook_config=lowerCAmelCase__					,     hyperparameters={
													        **self.env.hyperparameters,
													        '''model_name_or_path''': self.model_name_or_path,
													        '''max_steps''': 5_00,
													    }					,     metric_definitions=self.env.metric_definitions					,     distribution=lowerCAmelCase__					,     py_version='''py36'''					,     )







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   int					):

													"""simple docstring"""
													TrainingJobAnalytics(lowerCAmelCase__					).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv"""					)







						@parameterized.expand([(1,)]					)
						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   List[Any]					):

													"""simple docstring"""
													# create estimator
													SCREAMING_SNAKE_CASE       :   Any         =      self.create_estimator(lowerCAmelCase__					)

													# run training
													estimator.fit()

													# result dataframe
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      TrainingJobAnalytics(estimator.latest_training_job.name					).dataframe()

													# extract kpis
													SCREAMING_SNAKE_CASE       :   int         =      list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value''']					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value''']					)
													# get train time from SageMaker job, this includes starting, preprocessing, stopping
													SCREAMING_SNAKE_CASE       :   int         =      (
													    Session().describe_training_job(estimator.latest_training_job.name					).get('''TrainingTimeInSeconds'''					,     99_99_99					)
													)

													# assert kpis
													assert train_runtime <= self.results["train_runtime"]
													assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy					)
													assert all(t <= self.results['''eval_loss'''] for t in eval_loss					)

													# dump tests result into json file to share in PR
													with open(F"""{estimator.latest_training_job.name}.json"""					,     '''w'''					) as outfile:
																				json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss}					,     lowerCAmelCase__					)




